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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - gpl-3.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
pretty_name: Tweets Hate Speech Detection
dataset_info:
  features:
    - name: label
      dtype:
        class_label:
          names:
            '0': no-hate-speech
            '1': hate-speech
    - name: tweet
      dtype: string
  splits:
    - name: train
      num_bytes: 3191888
      num_examples: 31962
    - name: test
      num_bytes: 1711606
      num_examples: 17197
  download_size: 4738708
  dataset_size: 4903494
train-eval-index:
  - config: default
    task: text-classification
    task_id: binary_classification
    splits:
      train_split: train
    col_mapping:
      tweet: text
      label: target
      metrics:
        - type: accuracy
          name: Accuracy
        - type: f1
          name: F1 binary
          args:
            average: binary
        - type: precision
          name: Precision macro
          args:
            average: macro
        - type: precision
          name: Precision micro
          args:
            average: micro
        - type: precision
          name: Precision weighted
          args:
            average: weighted
        - type: recall
          name: Recall macro
          args:
            average: macro
        - type: recall
          name: Recall micro
          args:
            average: micro
        - type: recall
          name: Recall weighted
          args:
            average: weighted

Dataset Card for Tweets Hate Speech Detection

Table of Contents

Dataset Description

Dataset Summary

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The tweets are primarily in English Language.

Dataset Structure

Data Instances

The dataset contains a label denoting is the tweet a hate speech or not

{'label': 0,  # not a hate speech
 'tweet': ' @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run'}

Data Fields

  • label : 1 - it is a hate speech, 0 - not a hate speech.
  • tweet: content of the tweet as a string.

Data Splits

The data contains training data with :31962 entries

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Crowdsourced from tweets of users

Who are the source language producers?

Cwodsourced from twitter

Annotations

Annotation process

The data has been precprocessed and a model has been trained to assign the relevant label to the tweet

Who are the annotators?

The data has been provided by Roshan Sharma

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of hatred/racist comments

Discussion of Biases

The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Roshan Sharma

Licensing Information

Information

Citation Information

Citation

Contributions

Thanks to @darshan-gandhi for adding this dataset.